Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2267
Publisher DOI: 10.3390/s19092094
Title: Learning environmental field exploration with computationally constrained underwater robots : Gaussian processes meet stochastic optimal control
Language: English
Authors: Dücker, Daniel-André 
Geist, Andreas René 
Kreuzer, Edwin 
Solowjow, Eugen 
Keywords: autonomous exploration;environmental field monitoring;Gaussian processes;Gaussian Markov random fields;Kalman filtering;stochastic optimal control
Issue Date: 6-May-2019
Publisher: Multidisciplinary Digital Publishing Institute
Source: Sensors 19 (9): 2094 (2019)
Journal or Series Name: Sensors 
Abstract (english): Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.
URI: http://hdl.handle.net/11420/2711
DOI: 10.15480/882.2267
ISSN: 1424-8220
Other Identifiers: doi: 10.3390/s19092094
Institute: Mechanik und Meerestechnik M-13 
Type: (wissenschaftlicher) Artikel
Funded by: Deutsche Forschungsgemeinschaft (DFG)
Project: grants Kr752/33-1 and Kr752/36-1 
Appears in Collections:Publications (tub.dok)

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